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[2602.20051] SEAL-pose: Enhancing 3D Human Pose Estimation via a Learned Loss for Structural Consistency
Summary
The SEAL-pose framework enhances 3D human pose estimation by utilizing a learned loss function that captures structural consistency among joints, outperforming traditional methods.
Why It Matters
This research addresses the limitations of conventional pose estimation techniques that rely on manual constraints, offering a data-driven approach that improves accuracy and efficiency in 3D human pose estimation, which is crucial for applications in robotics, animation, and augmented reality.
Key Takeaways
- SEAL-pose uses a learnable loss-net to enhance structural consistency in 3D human pose estimation.
- The framework outperforms traditional models with explicit structural constraints.
- Extensive experiments show reduced per-joint errors across various benchmarks.
- The method is effective in cross-dataset and in-the-wild settings.
- SEAL-pose demonstrates the potential of data-driven approaches in improving pose estimation accuracy.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20051 (cs) [Submitted on 23 Feb 2026] Title:SEAL-pose: Enhancing 3D Human Pose Estimation via a Learned Loss for Structural Consistency Authors:Yeonsung Kim, Junggeun Do, Seunguk Do, Sangmin Kim, Jaesik Park, Jay-Yoon Lee View a PDF of the paper titled SEAL-pose: Enhancing 3D Human Pose Estimation via a Learned Loss for Structural Consistency, by Yeonsung Kim and 5 other authors View PDF HTML (experimental) Abstract:3D human pose estimation (HPE) is characterized by intricate local and global dependencies among joints. Conventional supervised losses are limited in capturing these correlations because they treat each joint independently. Previous studies have attempted to promote structural consistency through manually designed priors or rule-based constraints; however, these approaches typically require manual specification and are often non-differentiable, limiting their use as end-to-end training objectives. We propose SEAL-pose, a data-driven framework in which a learnable loss-net trains a pose-net by evaluating structural plausibility. Rather than relying on hand-crafted priors, our joint-graph-based design enables the loss-net to learn complex structural dependencies directly from data. Extensive experiments on three 3D HPE benchmarks with eight backbones show that SEAL-pose reduces per-joint errors and improves pose plausibility compared with the corresponding backbones across all settings. Beyond...